diff --git a/docs/chapter_2.html b/docs/chapter_2.html index 390cc86..572ab70 100644 --- a/docs/chapter_2.html +++ b/docs/chapter_2.html @@ -7,7 +7,7 @@ -Hacking Religion: TRS & Data Science in Action - 2  Getting into the nitty-gritty details +Hacking Religion: TRS & Data Science in Action - 2  Different ways to measure religion using data science - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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1  Set up local workspace:

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In this chapter we’re going to do some exciting things with census data. This is a very important dataset, often analysed, but much less frequently with regards to the subject of religion and almost never with the level of granularity you’ll learn to work with over the course of this chapter.

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We’ll get to the good stuff in a moment, but first we need to do a bit of setup. The code provided here is intended to set up your workspace and is also necessary for the quarto application we use to build this book. If you hadn’t already noticed, this book is also generated by live (and living!) R code. Quarto is an application which blends together text and blocks of code to produce books. You can ignore most of it for now, though if you’re running the code as we go along, you’ll definitely want to include these lines, as they create directories where your files will go as you create charts and extract data below and tells R where to find those files:

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setwd("/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion")
-library(here)  |> suppressPackageStartupMessages()
-library(tidyverse)  |> suppressPackageStartupMessages()
-here::i_am("chapter_1.qmd")
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here() starts at /Users/kidwellj/gits/hacking_religion_textbook/hacking_religion
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if (dir.exists("data") == FALSE) {
-  dir.create("data") 
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-if (dir.exists("figures") == FALSE) {
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-if (dir.exists("derivedData") == FALSE) {
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2 Introducing the 2021 UK Census

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For our first exercise in this book, we’re going to work with a census dataset. As you’ll see by contrast in chapter 2, census data is intended to represent as fully as possible the demographic features of a specific community, in this case, the United Kingdom. We might assume that a large-scale survey given to 1000 or more respondents and distributed appropriately across a variety of demographics will approximate the results of a census, but there’s really no substitite for a survey which has been given to (nearly) the entire population. This also allows us to compare a number of different subsets, as we’ll explore further below. The big question that we’re confronting in this chapter is how best to represent religious belonging and participation at such a large scale, and to flag up some of the hidden limitations in this seemingly comprehensive dataset.

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3 Getting started with UK Census data

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Let’s start by importing some data into R. Because R is what is called an object-oriented programming language, we’ll always take our information and give it a home inside a named object. There are many different kinds of objects, which you can specify, but usually R will assign a type that seems to fit best, often a table of data which looks a bit like a spreadsheet which is called a dataframe.

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If you’d like to explore this all in a bit more depth, you can find a very helpful summary in R for Data Science, chapter 8, “data import”.
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In the example below, we’re going to begin by reading in data from a comma separated value file (“csv”) which has rows of information on separate lines in a text file with each column separated by a comma. This is one of the standard plain text file formats. R has a function you can use to import this efficiently called read.csv. Each line of code in R usually starts with the object, and then follows with instructions on what we’re going to put inside it, where that comes from, and how to format it:

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uk_census_2021_religion <- read.csv(here("example_data", "census2021-ts030-rgn.csv"))
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4 Examining data:

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What’s in the table? You can take a quick look at either the top of the data frame, or the bottom using one of the following commands:

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head(uk_census_2021_religion)
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                 geography   total no_religion christian buddhist  hindu jewish
-1               North East 2647012     1058122   1343948     7026  10924   4389
-2               North West 7417397     2419624   3895779    23028  49749  33285
-3 Yorkshire and The Humber 5480774     2161185   2461519    15803  29243   9355
-4            East Midlands 4880054     1950354   2214151    14521 120345   4313
-5            West Midlands 5950756     1955003   2770559    18804  88116   4394
-6                     East 6335072     2544509   2955071    26814  86631  42012
-  muslim   sikh other no_response
-1  72102   7206  9950      133345
-2 563105  11862 28103      392862
-3 442533  24034 23618      313484
-4 210766  53950 24813      286841
-5 569963 172398 31805      339714
-6 234744  24284 36380      384627
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This is actually a fairly ugly table, so I’ll use an R tool called kable to give you prettier tables in the future, like this:

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knitr::kable(head(uk_census_2021_religion))
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geographytotalno_religionchristianbuddhisthindujewishmuslimsikhotherno_response
North East26470121058122134394870261092443897210272069950133345
North West7417397241962438957792302849749332855631051186228103392862
Yorkshire and The Humber548077421611852461519158032924393554425332403423618313484
East Midlands4880054195035422141511452112034543132107665395024813286841
West Midlands5950756195500327705591880488116439456996317239831805339714
East6335072254450929550712681486631420122347442428436380384627
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You can see how I’ve nested the previous command inside the kable command. For reference, in some cases when you’re working with really complex scripts with many different libraries and functions, they may end up with functions that have the same name, and you may unwittingly run a function from the wrong library. You can specify the library where the function is meant to come from by preceding it with :: as we’ve done knitr:: above. The same kind of output can be gotten using tail which shows the final lines of a given data object:

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knitr::kable(tail(uk_census_2021_religion))
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geographytotalno_religionchristianbuddhisthindujewishmuslimsikhotherno_response
5West Midlands5950756195500327705591880488116439456996317239831805339714
6East6335072254450929550712681486631420122347442428436380384627
7London87997282380404357768177425453034145466131875414454386759615662
8South East92780683733094431331954433154748186823090677434854098566279
9South West5701186251336926358722457927746738780152746536884367732
10Wales3107494144639813547731007512242204466947404815926195041
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5 Parsing and Exploring your data

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The first thing you’re going to want to do is to take a smaller subset of a large data set, either by filtering out certain columns or rows. Let’s say we want to just work with the data from the West Midlands and we’d like to omit some of the other columns which relate to different geographic areas. We can choose a specific range of columns using select, like this:

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You can use the filter command to do this. To give an example, filter can pick a single row in the following way:

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uk_census_2021_religion_wmids <- uk_census_2021_religion %>% filter(geography=="West Midlands")
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In the line above, you’ll see that we’ve created a new object which contains this more specific subset of the original data. You can also overwrite your original object with the new information, and as you go along you’ll need to make decisions about whether to keep many iterations as different objects, or if you want to try and hold onto only the bare essentials.

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It’s also worth noting that there are only a few rules for naming objects (you can’t have spaces, for one thing), so you’ll want to come up with a specific convention that works for you. I tend to assign a name for each object that indicates the dataset it has come from and then chain on further names using underscore characters which indicate what kind of subset it is. You may want to be careful about letting your names get too long, and find comprehensible ways to abbreviate.

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Now we’ll use select in a different way to narrow our data to specific columns that are needed (no totals!).

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Some readers will want to pause here and check out Hadley Wickham’s “R For Data Science” book, in the section, “Data visualisation” to get a fuller explanation of how to explore your data.
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In keeping with my goal to demonstrate data science through examples, we’re going to move on to producing some snappy looking charts for this data.

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6 Making your first data visulation: the humble bar chart

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We’ve got a nice lean set of data, so now it’s time to visualise this. We’ll start by making a pie chart:

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uk_census_2021_religion_wmids <- uk_census_2021_religion_wmids %>% select(no_religion:no_response)
-uk_census_2021_religion_wmids <- gather(uk_census_2021_religion_wmids)
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There are two basic ways to do visualisations in R. You can work with basic functions in R, often called “base R” or you can work with an alternative (and extremely popular) library called ggplot which aims to streamline the coding you need to make a chart:

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6.1 Base R

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Here’s the code you can use to create a new data object which contains the information necessary for our chart. I’ve just used the generic name “df” because we won’t hold on to this chart. You’ll also see that I’ve organised the data in descending order using the base R function order(). In the next line, we use the Base R function “barplot” to create a chart.

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df <- uk_census_2021_religion_wmids[order(uk_census_2021_religion_wmids$value,decreasing = TRUE),]
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6.2 GGPlot

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The conventions of GGPlot take a bit of getting used to, but it’s a very powerful tool which will scale to quite complicated charts.

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This initial chart doesn’t include a “totals” column, as it isn’t in the data and these plotting tools simply represent whatever data you put into them. It’s nice to have a list of sums for each column, and this is pretty easy to do in R. As you’ll see below, we are going to take the original table, and overwrite it with a new column added:

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1uk_census_2021_religion_totals <- uk_census_2021_religion %>% select(no_religion:no_response)
-uk_census_2021_religion_totals <- uk_census_2021_religion_totals %>%
-2   summarise(across(everything(), ~ sum(., na.rm = TRUE)))
-3uk_census_2021_religion_totals <- gather(uk_census_2021_religion_totals)
-4ggplot(uk_census_2021_religion_totals, aes(x= reorder(key,-value),value)) + geom_bar(stat ="identity")
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You might notice that these two dataframes give us somewhat different results. But with data science, it’s much more interesting to compare these two side-by-side in a visualisation. We can join these two dataframes and plot the bars side by side using bind() - which can be done by columns with cbind() and rows using rbind():

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uk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)
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Do you notice there’s going to be a problem here? How can we tell one set from the other? We need to add in something idenfiable first! To do this we can simply create a new column for each with identifiable information before we bind them:

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uk_census_2021_religion_totals$dataset <- c("totals")
-uk_census_2021_religion_wmids$dataset <- c("wmids")
-uk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)
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Now we’re ready to plot out our data as a grouped barplot:

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If you’re looking closely, you will notice that I’ve added two elements to our previous ggplot. I’ve asked ggplot to fill in the columns with reference to the dataset column we’ve just created. Then I’ve also asked ggplot to alter the position="dodge" which places bars side by side rather than stacked on top of one another. You can give it a try without this instruction to see how this works. We will use stacked bars in a later chapter, so remember this feature.

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If you inspect our chart, you can see that we’re getting closer, but it’s not really that helpful to compare the totals. What we need to do is get percentages that can be compared side by side. This is easy to do using another dplyr feature mutate:

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You can find a helpful write-up about dplyr by Antoine Soetewey at, “Stats and R”.
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It’s worth noting that an alternative approach is to leave the numbers intact and simply label them differently so they render as percentages on your charts. You can do this with the `scales() library and the label_percent() function. The downside of this approach is that it won’t transfer to tables if you make them.
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uk_census_2021_religion_totals <- uk_census_2021_religion_totals %>% 
-  dplyr::mutate(perc = scales::percent(value / sum(value), accuracy = 0.1, trim = FALSE))
-uk_census_2021_religion_wmids <- uk_census_2021_religion_wmids %>% 
-  dplyr::mutate(perc = scales::percent(value / sum(value), accuracy = 0.1, trim = FALSE))
-uk_census_2021_religion_merged <- rbind(uk_census_2021_religion_totals, uk_census_2021_religion_wmids)
-ggplot(uk_census_2021_religion_merged, aes(fill=dataset, x=key, y=perc)) + geom_bar(position="dodge", stat ="identity")
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This chart gives us a comparison which sets bars from the West Midlands data and UK-wide total data side by side for each category. The same principles that we’ve used here can be applied to draw in more data. You could, for example, compare census data from different years, e.g. 2001 2011 and 2021, as we’ll do below. Our use of dplyr::mutate above can be repeated to add an infinite number of further series’ which can be plotted in bar groups.

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We’ll draw this data into comparison with later sets in the next chapter. But the one glaring issue which remains for our chart is that it’s lacking in really any aesthetic refinements. This is where ggplot really shines as a tool as you can add all sorts of things.

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The ggplot tool works by stacking additional elements on to your original plot using +. So, for example, let’s say we want to improve the colours used for our bars. You can specify the formatting for the fill on the scale by tacking on scale_fill_brewer. This uses a particular tool (and a personal favourite of mine) called colorbrewer. Part of my appreciation of this tool is that you can pick colours which are not just visually pleasing, and produce useful contrast / complementary schemes, but you can also work proactively to accommodate colourblindness. Working with colour schemes which can be divergent in a visually obvious way will be even more important when we work on geospatial data and maps in a later chapter.

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ggplot(uk_census_2021_religion_merged, aes(fill=dataset, x=key, y=perc)) + geom_bar(position="dodge", stat ="identity") + scale_fill_brewer(palette = "Set1")
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We might also want to add a border to our bars to make them more visually striking (notice the addition of color to the geom_bar below. I’ve also added reorder() to the x value to sort descending from the largest to smallest.

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You can find more information about reordering ggplots on the R Graph gallery.
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uk_census_2021_religion_merged$dataset <- factor(uk_census_2021_religion_merged$dataset, levels = c('wmids', 'totals'))
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We can fine tune a few other visual features here as well, like adding a title with ggtitle and some prettier fonts with theme_ipsum() (which requires the hrbrthemes() library). We can also remove the x and y axis labels (not the data labels, which are rather important).

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ggplot(uk_census_2021_religion_merged, aes(fill=fct_reorder(dataset, value), x=reorder(key,-value),value, y=perc)) + geom_bar(position="dodge", stat ="identity", colour = "black") + scale_fill_brewer(palette = "Set1") + ggtitle("Religious Affiliation in the UK: 2021") + xlab("") + ylab("")
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It’s also a bit hard to read our Y-axis labels with everything getting cramped down there, so let’s rotate that text to 180 degrees so those labels are clear:

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ggplot(uk_census_2021_religion_merged, aes(fill=fct_reorder(dataset, value), x=reorder(key,-value),value, y=perc)) + geom_bar(position="dodge", stat ="identity", colour = "black") + scale_fill_brewer(palette = "Set1") + ggtitle("Religious Affiliation in the UK: 2021") + xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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7 Telling the truth in data science: Is your chart accurate?

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If you’ve been following along up until this point, you’ll have produced a fairly complete data visualisation for the UK census. There is some technical work yet to be done fine-tuning the visualisation of our chart here, but I’d like to pause for a moment and consider an ethical question drawn from the principles I outlined in the introduction: is the title of this chart truthful and accurate?

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On one hand, it is a straight-forward reference to the nature of the question asked on the 2021 census survey instrument, e.g. something like “what is your religious affiliation”. However, as you will see in the next chapter, other large data sets from the same year which involved a similar question yielded different results. Part of this could be attributed to the amount of non-respose to this specific question which, in the 2021 census is between 5-6% across many demographics. It’s possible (though perhaps unlikely) that all those non-responses were (to pick one random example) Jedi religion practitioners who felt uncomfortable identifying themselves on such a census survey. If even half of the non-responses were of this nature, this would dramatically shift the results especially in comparison to other minority groups. So there is some work for us to do here in representing non-response as a category on the census.

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It’s equally possible that someone might feel uncertain when answering, but nonetheless land on a particular decision marking “Christian” when they wondered if they should instead tick “no religion. Some surveys attempt to capture uncertainty in this way, asking respondents to mark how confident they are about their answers, or allowing respondents to choose multiple answers, but the makers of the census made a specific choice not to capture this so we simply don’t know. It’s possible that a large portion of respondents in the”Christian” category were hovering between this and another response and they might shift their answers when responding on a different day or in the context of a particular experience like a good or bad day attending church, or perhaps having just had a conversation with a friend which shifted their thinking.

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Even the inertia of survey design can have an effect on this, so responding to other questions in a particular way, thinking about ethnic identity, for example, can prime a person to think about their religious identity in a different or more focussed way, altering their response to the question. If someone were to ask you on a survey “are you hungry” you might say “no,” but if they’d previously asked you a hundred questions about your favourite pizza toppings you might have been primed to think about food and when you arrive at the same question, even at the same time in the day, your answer would be an enthusiastic “yes”. This can be the case for some ethnicity and religion pairings which may have priming interrelations, which we’ll explore a bit more in the next chapter.

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Given this challenge, some survey instruments randomise the order of questions. This hasn’t been done on the census (which would have been quite hard work given that most of the instruments were printed hard copies!), so again, we can’t really be sure if those answers given are stable in such a way.

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Finally, researchers have also found that when people are asked to mark their religious affiliation, sometimes they can prefer to mark more than one answer. A person might consider themselves to be “Muslim” but also “Spiritual but not religious” preferring the combination of those identities. It is also the case that respondents do in practice identify with less expected hybrid religious identities as well, such as “Christian” and “Hindu”. One might assume that these are different religions without many doctrinal overlaps, but researchers have found that in actual practice, it’s perfectly possible for some people to inhabit two or more categories which the researcher might assume are opposed.

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The UK census only allows respondents to tick a single box for the religion category. It is worth noting that, in contrast, the responses for ethnicity allow for combinations. Given that this is the case, it’s impossible to know which way a person went at the fork in the road as they were forced to choose just one half of this kind of hybrid identity. Did they feel a bit more Buddhist that day? Or spiritual?

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Finally, it is interesting for us to consider exactly what it means for a person when they tick a box like this. The census doesn’t specify how one should calculate the basis of your participation. Is it because they attend synagogue on a weekly basis? Some persons would consider weekly attendance at workship a prerequisite for membership in a group, but others would not. Indeed we can infer from surveys and research which aims to track rates of participation in weekly worship that many people who tick boxes for particular religious identities on the census have never attended a worship service at all.

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What does this mean for our results? Are they completely unreliable and invalid? I don’t think this is the case or that taking a clear-eyed look at the force and stability of our underlying data should be cause for despair. Instead, the most appropriate response is humility. Someone has made a statement which is recorded in the census, of this we can be sure. They felt it to be an accurate response on some level based on the information they had at the time. And with regard to the census, it is a massive dataset, covering much of the population, and this large sample size does afford some additional validity. The easiest way to represent all this reality in the form of speaking truthfully about our data is to acknowledge that however valid it may seem, it is nonetheless a snapshot. For this reason, I would always advise that the best title for a chart is one which specifies the data set.

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So if we are going to fine-tune our visuals to ensure they comport with our hacker principles and speak truthfully, we should also probably do something different with those non-responses:

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ggplot(uk_census_2021_religion_merged, aes(fill=fct_reorder(dataset, value), x=reorder(key,-value),value, y=perc)) + geom_bar(position="dodge", stat ="identity", colour = "black") + scale_fill_brewer(palette = "Set1") + ggtitle("Religious Affiliation in the 2021 Census of England and Wales") + xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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8 Multifactor Visualisation

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One element of R data analysis of census datasets that can get really interesting is working with multiple variables. Above we’ve looked at the breakdown of religious affiliation across the whole of England and Wales (Scotland operates an independent census so we haven’t included it here) and by placing this data alongside a specific region, we’ve already made a basic entry into working with multiple variables but this can get much more interesting. Adding an additional quantitative variable (also known as bivariate data when you have two variables) into the mix, however can also generate a lot more information and we have to think about visualising it in different ways which can still communicate with visual clarity in spite of the additional visual noise which is inevitable with enhanced complexity. Let’s have a look at the way that religion in England and Wales breaks down by ethnicity.

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For the UK, census data is made available for programmatic research like this via an organisation called NOMIS. Luckily for us, there is an R library you can use to access nomis directly which greatly simplifies the process of pulling data down from the platform. It’s worth noting that if you’re not in the UK, there are similar options for other countries. Nearly every R textbook I’ve ever seen works with USA census data (which is part of the reason I’ve taken the opportunity to work with a different national census dataset here in this book), so you’ll find plenty of documentation available on the tools you can use for US Census data. Similarly for the EU, Canada, Austrailia etc.

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If you want to draw some data from the nomis platform yourself in R, have a look at the nomis script in our companion cookbook repository. For now, we’ll provide some data extracts for you to use.

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Let’s start by loading in some of the enhanced tables from nomis with the 2021 religion / ethnicity tables:

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nomis_extract_census2021 <- readRDS(file = (here("example_data", "nomis_extract_census2021.rds")))
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I’m hoping that readers of this book will feel free to pause along the way and “hack” the code to explore questions of their own, perhaps in this case probing the NOMIS data for answers to their own questions. If I tidy things up too much, however, you’re likely to be surprised when you get to the real life data sets. So that you can use the code in this book in a reproducible way, I’ve started this exercise with what is a more or less raw dump from NOMIS. This means that the data is a bit messy and needs to be filtered down quite a bit so that it only includes the basic stuff that we’d like to examine for this particular question. The upside of this is that you can modify this code to draw in different columns etc.

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1uk_census_2021_religion_ethnicity <- select(nomis_extract_census2021, GEOGRAPHY_NAME, C2021_RELIGION_10_NAME, C2021_ETH_8_NAME, OBS_VALUE)
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-2uk_census_2021_religion_ethnicity <- filter(uk_census_2021_religion_ethnicity, GEOGRAPHY_NAME=="England and Wales" & C2021_RELIGION_10_NAME != "Total" & C2021_ETH_8_NAME != "Total")
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-3uk_census_2021_religion_ethnicity <- filter(uk_census_2021_religion_ethnicity, C2021_ETH_8_NAME != "White: English, Welsh, Scottish, Northern Irish or British" & C2021_ETH_8_NAME != "White: Irish" & C2021_ETH_8_NAME != "White: Gypsy or Irish Traveller, Roma or Other White")
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-ggplot(uk_census_2021_religion_ethnicity, aes(fill=C2021_ETH_8_NAME, x=C2021_RELIGION_10_NAME, y=OBS_VALUE)) +
-  geom_bar(position="dodge", stat ="identity", colour = "black") + 
-  scale_fill_brewer(palette = "Set1") + 
-  ggtitle("Religious Affiliation in the 2021 Census of England and Wales") + 
-  xlab("") + ylab("") + 
-4  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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-Select relevant columns -
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-Filter down to simplified dataset with England / Wales and percentages without totals -
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-The 2021 census data includes white sub-groups so we need to omit those -
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-Let’s plot it out and see how things look! -
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The trouble with using grouped bars here, as you can see, is that there are quite sharp disparities which make it hard to compare in meaningful ways. We could use logarithmic rather than linear scaling as an option, but this is hard for many general public audiences to appreciate without guidance. One alternative quick fix is to extract data from “white” respondents which can then be placed in a separate chart with a different scale.

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Usually, when we display data we think of numbers in a linear way, that is, each centimetre of the x-axis on our chart represents the same quantity as the cm above and below it. This is generally a preferred way to display data, and as close to a “common sense” way of showing things as we might get. However, this kind of linear visualisation works best only in cases where the difference between one category on our chart and the next is relatively uniform. This is, for the most part, the case with our charts above. However, we’ve hit another scenario here, the difference between the “White” subcategory and all the others is large enough that those other four categories aren’t really easily perceived on our chart. One way to address this is to leave behind a linear approach to displaying that x-axis data. What if, for example, each step up on our chart didn’t represent the same amount of value, e.g. 10, 20, 30, 40, 50 etc. but instead represented an increase which followed orders of magnitude, so something more like 10, 100, 1000, 10000, etc. That’s the essence of a logarithmic visualisation, which can much more easily display data that has a very large range or with disparities from one category to another.

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1uk_census_2021_religion_ethnicity_white <- filter(uk_census_2021_religion_ethnicity, C2021_ETH_8_NAME == "White")
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-2uk_census_2021_religion_ethnicity_nonwhite <- filter(uk_census_2021_religion_ethnicity, C2021_ETH_8_NAME != "White")
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-3ggplot(uk_census_2021_religion_ethnicity_nonwhite, aes(fill=C2021_ETH_8_NAME, x=C2021_RELIGION_10_NAME, y=OBS_VALUE)) + geom_bar(position="dodge", stat ="identity", colour = "black") + scale_fill_brewer(palette = "Set1") + ggtitle("Religious Affiliation in the 2021 Census of England and Wales") + xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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-Filter down to simplified dataset with England / Wales and percentages without totals -
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-Filtering with != allows us to create a subset where that response is excluded -
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-Let’s plot it out and see where we’ve gotten to! -
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As you’ll notice, this is a bit better, but this still doesn’t quite render with as much visual clarity and communication as I’d like. Another approach we can take is to represent each bar as a percentage of the total for that ethnicity subgroup rather than as raw values. We can do this by adding an extra step to our visualisation drawing on the mutate() function which enables us to create a series of groups based on a specific column (e.g. C2021_ETH_8_NAME) and then create an additional column in our dataframe which represents values within each of our groups as percentages of the total rather than raw values:

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uk_census_2021_religion_ethnicity_percents <- uk_census_2021_religion_ethnicity %>%
-  group_by(C2021_ETH_8_NAME) %>%
-  mutate(Percentage = OBS_VALUE / sum(OBS_VALUE) * 100)
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-ggplot(uk_census_2021_religion_ethnicity_percents, aes(fill=C2021_ETH_8_NAME, x=C2021_RELIGION_10_NAME, y=Percentage)) +
-  geom_bar(position="dodge", stat ="identity", colour = "black") + 
-  scale_fill_brewer(palette = "Set1") + 
-  ggtitle("Religious Affiliation in the 2021 Census of England and Wales") + 
-  xlab("") + ylab("") + 
-  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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As you can see, this gives us a really different sense of representation within each group. Another option we can use here is a technique in R called “faceting” which creates a series of small charts which can be viewed alongside one another. This is just intended to whet you appetite for facetted plots, so I won’t break down all the separate elements in great detail as there are other guides which will walk you through the full details of how to use this technique if you want to do a deep dive. For now, you’ll want to observe that we’ve augmented the ggplot with a new element called facet_wrap which takes the ethnicity data column as the basis for rendering separate charts.

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ggplot(uk_census_2021_religion_ethnicity_nonwhite, aes(x=C2021_RELIGION_10_NAME, y=OBS_VALUE)) + 
-  geom_bar(position="dodge", stat ="identity", colour = "black") + 
-  facet_wrap(~C2021_ETH_8_NAME, ncol = 2) + scale_fill_brewer(palette = "Set1") + 
-  ggtitle("Religious Affiliation in the 2021 Census of England and Wales") + xlab("") + ylab("") + 
-  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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That’s a bit better! Now we have a much more accessible set of visual information which compares across categories and renders most of the information we’re trying to capture.

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To take this chart just one step further, I’d like to take the faceted chart we’ve just done and add in totals for the previous two census years (2001 and 2011) so we can see how trends are changing in terms of religious affiliation within ethnic self-identification categories. We’ll draw on some techniques we’re already developed above using rbind() to connect up each of these charts (after we’ve added a column identifying each chart by the census year). We will also need to use one new technique to change the wording of ethnic categories as this isn’t consistent from one census to the next and ggplot will struggle to chart things if the terms being used are exactly the same. We’ll use mutate() again to accomplish this with some slightly different code.

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First we need to get the tables of Census 2011 and 2001 religion data from nomis:

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nomis_extract_census2001 <- readRDS(file = (here("example_data", "nomis_extract_census2001.rds")))
-nomis_extract_census2011 <- readRDS(file = (here("example_data", "nomis_extract_census2011.rds")))
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Next, as we’ve already done above, we need to filter and tidy the tables:

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1uk_census_2001_religion_ethnicity <- select(nomis_extract_census2001, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHHUK11_NAME, OBS_VALUE)
-uk_census_2011_religion_ethnicity <- select(nomis_extract_census2011, GEOGRAPHY_NAME, C_RELPUK11_NAME, C_ETHPUK11_NAME, OBS_VALUE)
-2uk_census_2001_religion_ethnicity <- filter(uk_census_2001_religion_ethnicity, GEOGRAPHY_NAME=="England and Wales" & C_RELPUK11_NAME != "All categories: Religion")
-uk_census_2011_religion_ethnicity <- filter(uk_census_2011_religion_ethnicity, GEOGRAPHY_NAME=="England and Wales" & C_RELPUK11_NAME != "All categories: Religion" & C_ETHPUK11_NAME != "All categories: Ethnic group")
-3uk_census_2001_religion_ethnicity <- uk_census_2001_religion_ethnicity %>% filter(grepl('Total', C_ETHHUK11_NAME))
-uk_census_2011_religion_ethnicity <- uk_census_2011_religion_ethnicity %>% filter(grepl('Total', C_ETHPUK11_NAME))
-uk_census_2011_religion_plot <- ggplot(uk_census_2011_religion_ethnicity, aes(x = C_RELPUK11_NAME, y = OBS_VALUE)) + geom_bar(stat = "identity") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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-Select columns -
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-Filter down to simplified dataset with England / Wales and percentages without totals -
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-Drop unnecessary columns -
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The bind tool we’re going to use is very picky and expects everything to match perfectly so that it doesn’t join up data that is unrelated. Unfortunately, the census table data format has changed in each decade, so we need to harmonise the column titles so that we can join the data and avoid confusing R. This is a pretty common problem you’ll face in working with multiple datasets in the same chart, so well worth noticing the extra necessary step here.

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1uk_census_2001_religion_ethnicity$dataset <- c("2001")
-uk_census_2011_religion_ethnicity$dataset <- c("2011")
-uk_census_2021_religion_ethnicity$dataset <- c("2021")
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-2names(uk_census_2001_religion_ethnicity) <- c("Geography", "Religion", "Ethnicity", "Value", "Year")
-names(uk_census_2011_religion_ethnicity) <- c("Geography", "Religion", "Ethnicity", "Value", "Year")
-names(uk_census_2021_religion_ethnicity) <- c("Geography", "Religion", "Ethnicity", "Value", "Year")
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-First we add a column to each dataframe letting us know which census year it is from so we don’t lose track of the census it comes from when they’re all combined into a single table. -
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Now that we have column titles all sorted, we also need to adjust the category descriptions as the formatting has also changed in subsequent decades. To do this, we’ll use the very handy tool mutate which is a bit like a “find and replace text” tool in R:

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# Next we need to change the terms using mutate()
-uk_census_2001_religion_ethnicity <- uk_census_2001_religion_ethnicity %>% 
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^White: Total$", replacement = "White")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Mixed: Total$", replacement = "Mixed")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Asian: Total$", replacement = "Asian")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Black or Black British: Total$", replacement = "Black")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Chinese or Other ethnic group: Total$", replacement = "Other"))
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-uk_census_2011_religion_ethnicity <- uk_census_2011_religion_ethnicity %>% 
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^White: Total$", replacement = "White")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Mixed/multiple ethnic group: Total$", replacement = "Mixed")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Asian/Asian British: Total$", replacement = "Asian")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Black/African/Caribbean/Black British: Total$", replacement = "Black")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Other ethnic group: Total$", replacement = "Other"))
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-uk_census_2021_religion_ethnicity <- uk_census_2021_religion_ethnicity %>% 
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^White: Total$", replacement = "White")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Mixed or Multiple ethnic groups$", replacement = "Mixed")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Asian, Asian British or Asian Welsh$", replacement = "Asian")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Black, Black British, Black Welsh, Caribbean or African$", replacement = "Black")) %>%
-  mutate(Ethnicity = str_replace_all(Ethnicity, 
-            pattern = "^Other ethnic group$", replacement = "Other"))
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Now that we have all the columns and data in formats which will match for merge, let’d do the merge! This is only two (rather than three operations) as we combine 2021 and 2011 and then do a second combine that grafts in the 2001 data:

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uk_census_merged_religion_ethnicity <- rbind(uk_census_2021_religion_ethnicity, uk_census_2011_religion_ethnicity)
-uk_census_merged_religion_ethnicity <- rbind(uk_census_merged_religion_ethnicity, uk_census_2001_religion_ethnicity)
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As we realised in the work above, we need to split out non-white and white data so that the data is visually comprehensible:

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uk_census_merged_religion_ethnicity_nonwhite <- filter(uk_census_merged_religion_ethnicity, Ethnicity != "White")
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Hopefully if everything went properly, we can now do an initial ggplot to see how things look side-by-side:

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ggplot(uk_census_merged_religion_ethnicity_nonwhite, aes(fill=Year, x=Religion, y=Value)) + 
-  geom_bar(position="dodge", stat ="identity", colour = "black") + 
-  facet_wrap(~Ethnicity, ncol = 2) + 
-  scale_fill_brewer(palette = "Set1") + 
-  ggtitle("Religious Affiliation in the 2001-2021 Census of England and Wales") + 
-  xlab("") + ylab("") + 
-  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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We’re getting there, but as you can see there are a few formatting issues which remain. Our y-axis number labels are in scientific format which isn’t easy to read. You can use the very powerful and flexible scales() library to bring in some more readable formatting of numbers in a variety of places in R including in ggplot visualizations.

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library(scales) |> suppressPackageStartupMessages()
-ggplot(uk_census_merged_religion_ethnicity_nonwhite, aes(fill=Year, x=Religion, y=Value)) + geom_bar(position="dodge", stat ="identity", colour = "black") + facet_wrap(~Ethnicity, ncol = 2) + scale_fill_brewer(palette = "Set1") + scale_y_continuous(labels = unit_format(unit = "M", scale = 1e-6), breaks = breaks_extended(8)) + ggtitle("Religious Affiliation in the 2001-2021 Census of England and Wales") + xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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This chart shows an increase in almost every category for each decade, though it’s a bit hard to read in some cases. However, if we attend to our hacker principles, there’s another element here which can produce some misleading information. Consider for a moment how this information is based on the increase in raw numbers. It’s possbile that the numbers for each religion category may be going up, but population levels are also rising, and it’s possible here that the percentage share for a particular category may have gone up a bit less than population increase, e.g. the share of the population for that category has actually gone down. This is easy to fix and provide some more accurate information by normalising those figures based on the share of overall population for each decade. Let’s transform and visualise our data as percentages to see what kind of trends we can actually isolate:

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uk_census_merged_religion_ethnicity <- uk_census_merged_religion_ethnicity %>%
-  group_by(Ethnicity, Year) %>%
-  dplyr::mutate(Percent = Value/sum(Value))
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-ggplot(uk_census_merged_religion_ethnicity, aes(fill=Year, x=Religion, y=Percent)) + geom_bar(position="dodge", stat ="identity", colour = "black") + facet_wrap(~Ethnicity, scales="free_x") + scale_fill_brewer(palette = "Set1") + scale_y_continuous(labels = scales::percent) + ggtitle("Religious Affiliation in the 2001-2021 Censuses of England and Wales") + xlab("") + ylab("") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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Now you can see why this shift is important - the visualisation tells a completely different story in some cases across the two different charts. In the first (working off raw numbers) we see a net increase in Christianity across all categories. But if we take into account the fact that the overall share of population is growing for each of these groups, their actual composition is changing in a different direction. The proportion of each group is declining across the three census periods (albeit with an exception for the “Other” category from 2011 to 2021).

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To highlight a few of the technical features I’ve added for this final plot, I’ve used a specific feature within facet_wrap scales = "free_x" to let each of the individual facets adjust the total range on the x-axis. Since we’re looking at trends here and not absolute values, having correspondence across scales isn’t important and this makes for something a bit more visually tidy. I’ve also shifted the code for scale_y_continuous to render values as percentages (rather than millions).

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In case you want to print this plot out and hang it on your wall, you can use the ggsave tool to render the chart as an image file which you can print or email to a friend (or professor!):

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uk_census_merged_religion_ethnicity_plot <- ggplot(uk_census_merged_religion_ethnicity, aes(fill=Year, x=Religion, y=Percent)) + 
-  geom_bar(position="dodge", stat ="identity", colour = "black") + 
-  facet_wrap(~Ethnicity, scales="free_x") + 
-  scale_fill_brewer(palette = "Set1") + 
-  scale_y_continuous(labels = scales::percent) + 
-  ggtitle("Religious Affiliation in the 2001-2021 Censuses of England and Wales") + 
-  xlab("") + ylab("") + 
-  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
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-ggsave("figures/chart.png", plot=uk_census_merged_religion_ethnicity_plot, width = 8, height = 10, units=c("in"))
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That’s a pretty good day’s work. We’ve covered bifactorial analysis of the census data, compared this across years, and checked in each case to be sure that we’re representing the data accurately in the various visual elements of our charts. For the next chapter, we’re going to explore a wider range of ways to measure and represent religion.

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In the meantime, if you want to download the R code without all the commentary here so you can try running it in a browser, you can download that from the cookbook repository.

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Hacking Religion: TRS & Data Science in Action

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Jeremy H. Kidwell

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October 16, 2023

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Introduction: Hacking Religion

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Why this book?

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Data science is quickly consolidating as a new field, with new tools and user communities emerging every week. At the same time academic research has opened up into new interdisciplinary vistas, with experts crossing over into new fields, transgressing disciplinary boundaries and deploying tools in new and unexpected ways to develop knowledge. There are many gaps yet to be filled, but one which I found to be particularly glaring is the lack of applied data science documentation around the subject of religion. On one hand, scholars who are working with cutting edge theory seldom pick up these emerging tools of data science. On the other hand, data scientists rarely go beyond dabbling in religious themes, leaving quite a lot of really interesting theoretical research untouched. This book aims to bring these two things together: introducing the tools of data science in an applied way, whilst introducing some of the complexities and cutting edge theories which help us to conceptualise and frame our understanding of this knowledge regarding religion in the world around us.

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The hacker way

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It’s worth emphasising at the outset that this isn’t meant to be a generic data science book. My own training as a researcher lies in the field of religious ethics, and my engagement with digital technology has, from the very start, been a context for exploring matters of personal values and social action. A fair bit of ink has been spilled in books, magazines, blogs and zines unpacking what exactly it means to be a “hacker”. Pressing beyond some of the more superficial cultural stereotypes, I want to explain a bit here about how hacking can be a much more substantial vision for ethical engagement with technology and social transformation.

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Back in the 1980s Steven Levy tried to capture some of this in his book “Hackers: Heroes of the Computer Revolution”. As Levy put it, the “hacker ethic” included: (1) sharing, (2) openness, (3) decentralisation, (4) free access to computers and (5) world improvement. The key point here is that hacking isn’t just about writing and breaking code, or testing and finding weaknesses in computer systems and networks. There is often a more substantial underpinning ethical code which dovetails with on-the-surface matters of curiosity and craft.

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This emphasis on ethics is especially important when we’re doing data science because this kind of research work will put you in positions of influence. You might think this seems a bit overstated, but it never ceases to amaze me how much bringing a bar chart which succinctly shows a social trend can sway a conversation or decision making process. There is something unusually persuasive that comes with the combination of aesthetics, data and storytelling. I’ve met many people who have come to data science out of a desire to bring about social transformation in some sphere of life. People want to use technology and communication to make the world better. However, it’s possible that this can quickly get out of hand. With this in mind, I’ve found that it can be important to have a clear sense of the convictions that guide your work in this field: a “hacker code” of sorts. Here are the principles that I have settled on in my own practice of “hacking” religion:

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  1. Tell the truth: be candid about your limits, use visualisation responsibly
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  2. Work transparently: open data, open code
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  3. Work in community: draw others in by producing reproducible research
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  4. Work with reality and learn by doing
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It never ceases to amaze me how often people think that, when they’re working for something they think is important it is acceptable to conceal bad news or amplify good or compelling information beyond its real scope. There are always consequences, eventually. When people realise you’ve been misleading or manipulating them your platform and credibility will evaporate. Good work mixed with bad will all get tossed out. And sometimes, our convictions can lead us beyond an accurate and true apprehension of the situation we are focussed on in research.

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Presenting through “facts” an argument can become unnaturally compelling. Wrapping those facts up in something that uses colour, line and shape in a way that is aesthetically pleasing, even beautiful, enhances this allure even further. As you craft your own set of hacker principles, it’s vitally important that you always strive to tell the truth. This includes a willingness to acknowledge the limits of your information, and to share the whole set of information. The easiest way to do this is to work with visualation in a responsible way (I’ll get into this a bit more in Chapter 1) and to open up your data and code to scrutiny. By allowing others to try, criticise, edit, and reappropriate your code and data in their own ways, you contribute to knowledge and help to build up a community of accountability. The upside of this is that it’s also a lot more fun and interesting to work alongside others.

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Far too often, scholarly research (and theology) has been criticised for being disconnected from reality, making abstract pie-in-the-sky claims about how life should be lived. When exposed to the uncomfortable pressures of reality, these claims can crumble, or even turn sinister. One of the upsides of working with empirical research is that you have a chance to engage with the real world. For this reason, I love to do ethics in a way that arises - bottom-up - from real world experiences and relationships. There’s also the potential (at least in the best case scenario) that when we make choices based on reliable information drawn from everyday reality our policy and culture can be more resilient and accountable. This also works well with the hacker ethos of “learning by doing” and it’s this approach that guides my approach in this book. This isn’t just a book about data analysis, I’m proposing an approach which might be thought of as research-as-code, where you write out instructions to execute the various steps of work. The upside of this is that other researchers can learn from your work, correct and build on it as part of an intellectual commons. It takes a bit more time to learn and set things up, but the upside is that you’ll gain access to a set of tools and a research philosophy which is much more powerful.

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I’ll return to these principles periodically as we work through the coding and data in this book.

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Learning to code: my way

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Alongside these guiding principles, it’s also worth saying a bit about how I like to design teaching and learning. I remember when I was first starting out, and gathered coding manuals to read and learn from. They all tended to spend the first several hundred pages on theory, how you form an integer, data structures, subroutines, the logical structure of algorithms etc. etc. It was usually weeks of reading before I got to actually do anything. I know some people may prefer this approach, but I prefer a problem-focussed approach to learning. Give me something that is broken, or a problem to solve, which engages the things I want to figure out and the motivation for learning just comes much more naturally. And we know from research in cognitive science that these kinds of problem-focussed approaches can tend to faciliate faster learning and better retention. It will be helpful for you to be aware of this approach when you get into the book as it explains some of the editorial choices I’ve made and the way I’ve structured things.

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Each chapter focusses on a series of problems which are particularly salient for the use of data science to conduct research into religion. These problems will be my focal point, guiding choices of specific aspects of programming to introduce to you as we work our way around that dataset and some of the crucial questions that arise in terms of how we handle it. If you find this approach unsatisfying, luckily there are a number of really terrific guides which lay things out slowly and methodically and I will explicitly signpost some of these along the way so that you can do a “deep dive” when you feel like it. You can also find a list of resources in Appendix B to this book. Otherwise, I’ll take an accelerated approach to this introduction to data science in R. I expect that you will identify adjacent resources and perhaps even come up with your own creative approaches along the way, which incidentally is how real data science tends to work in practice.

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There are a range of terrific textbooks which cover all these elements in greater depth and more slowly. In particular, I’d recommend that many readers will want to check out Hadley Wickham’s “R For Data Science” book. I’ll include marginal notes in this guide pointing to sections of that book, and a few others which unpack the basic mechanics of R in more detail.

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Getting set up

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Every single tool, programming language and data set we refer to in this book is free and open source. These tools have been produced by professionals and volunteers who are passionate about data science and research and want to share it with the world, and in order to do this (and following the “hacker way”) they’ve made these tools freely available. This also means that you aren’t restricted to a specific proprietary, expensive, or unavailable piece of software to do this work. I’ll make a few opinionated recommendations here based on my own preferences and experience, but it’s really up to your own style and approach. In fact, given that this is an open source textbook, you can even propose additions to this chapter online sharing other tools you’ve found that you want to share with others.

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There are, right now, primarily two languages that statisticians and data scientists use for this kind of programmatic data science: python and R. Each language has its merits and I won’t rehash the debates between various factions. For this book, we’ll be using the R language. This is, in part, because I’ve found that the R user community and libraries tend to scale a bit better for the work that I’m commending in this book. However, it’s entirely possible that one could use python for all these exercises, and I’ll release a future version of this volume outlining python approaches to hacking religion.

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Bearing this in mind, the first step you’ll need to take is to download and install R. You can find instructions and install packages for a wide range of hardware on a key resource online for R programmers: The Comprehensive R Archive Network (or “CRAN”): https://cran.rstudio.com. Once you’ve installed R, you’ve got some choices to make about the kind of programming environment you’d like to use. You can just use a plain text editor like textedit to write your code and then execute your programs using the R software you’ve just installed. However, most users, myself included, tend to use an integrated development environment (or “IDE”). This is usually another software package with a guided user interface and some visual elements that make it faster to write and test your code. Some IDE packages will have built-in reference tools so you can look up options for libraries you use in your code. They will allow you to visualise the results of your code execution and perhaps most important of all, will enable you to execute your programs line by line so you can spot errors more quickly (we call this “debugging”). The two most popular IDE platforms for R coding at the time of writing this textbook are RStudio and Visual Studio. You should download and try out both and stick with your favourite, as the differences are largely aesthetic. I use a combination of RStudio and an enhanced plain text editor “Sublime Text” for my coding.

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Once you have R and your pick of an IDE, you are ready to go! Proceed to the next chapter and we’ll dive right in and get started!

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